Conference Paper: Study of seasonal forecasting of typhoon in East Asia

The 2014 Fall Meeting of the American Geophysical Union (AGU), Dan Francisco, CA., 15-19 December 2015. How to Cite?

Abstract

This study explores the feasibility of seasonal forecasts of typhoon in east Asia, especially Hong Kong and south China. Tropical cyclones are one of the dominating severe weather phenomena in the region, of which half reach typhoon strength (maximum winds of 118 kilometres per hour or more). The historical records show that typhoon usually occurs between May and November and brings increasing wind and heavy rainstorm. In this study, correlation are analyzed to check the effect of SST (Sea Surface Temperature) index of Nino3.4, SLP (Sea Level Pressure) index of SOI (Southern Oscillation Index), local temperature and rainfall with a lag period of 1-4 months on the occurrence and severity of typhoon. Significant positive correlations, with confidence level above 95%, are shown between target and Nino3.4 with lag of 1-3 months before the typhoon season. The results obtained from the multivariable regression models are found in reasonably good agreement with the observed distribution, 1 and 2 months ahead respectively.

The 2014 Fall Meeting of the American Geophysical Union (AGU), Dan Francisco, CA., 15-19 December 2015.

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http://hdl.handle.net/10722/217738

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Paper: H41A-0781

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dc.description.abstract

This study explores the feasibility of seasonal forecasts of typhoon in east Asia, especially Hong Kong and south China. Tropical cyclones are one of the dominating severe weather phenomena in the region, of which half reach typhoon strength (maximum winds of 118 kilometres per hour or more). The historical records show that typhoon usually occurs between May and November and brings increasing wind and heavy rainstorm. In this study, correlation are analyzed to check the effect of SST (Sea Surface Temperature) index of Nino3.4, SLP (Sea Level Pressure) index of SOI (Southern Oscillation Index), local temperature and rainfall with a lag period of 1-4 months on the occurrence and severity of typhoon. Significant positive correlations, with confidence level above 95%, are shown between target and Nino3.4 with lag of 1-3 months before the typhoon season. The results obtained from the multivariable regression models are found in reasonably good agreement with the observed distribution, 1 and 2 months ahead respectively.